AGENTIC-SYSTEMSJuly 16, 20261 min read2 views

OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets

Researchers have introduced OriginBlame, a record- and token-level data provenance system for AI training datasets. It precisely identifies data to be removed upon author request, eliminating catastrophic over-deletion and improving machine unlearning efficiency by 42%.

Computer Science > Artificial Intelligence

Title:OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets

View PDF HTML (experimental)Abstract:When a data contributor requests removal, model trainers face a practical gap: unlearning algorithms require a forget set, yet no tool can locate which training records belong to a given author. Existing provenance systems operate at file or dataset level, forcing catastrophic over-deletion. We present ob, a record- and token-level data provenance system that propagates author identity through data processing pipelines and resolves revocation requests into precise forget sets via deterministic queries. Evaluation on 219,555 Wikipedia pages demonstrates that record-level provenance eliminates dataset-level over-deletion (from 101x to 1.3x), while integration adds 1.3-4.0% throughput overhead (HuggingFace) and 2.1-19.0% (Datatrove) on wiki data. On a 1.7B model, provenance-based forget sets improve unlearning by 42% over random baselines.

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Source: arXiv cs.AI Recent

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